In this paper, we investigate the Max-Cut problem and propose a probabilistic heuristic to address its classic and weighted version. Our approach is based on the Estimation of Distribution Algorithm (EDA) that creates...
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Surface integrity of 3D medical imaging is crucial for surgery simulation or virtual diagnoses. However, undesirable holes often exist due to external damage on bodies or accessibility limitation on scanners. To bridg...
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Graph models offer high representational power and useful structural cues. Unfortunately, tracking objects by matching graphs over time is in general NP-hard. Simple appearance-based trackers are able to find temporal...
Vehicle detection is one of the key technologies in Intelligent Transportation System (ITS), and it i an important stage of vehicle tracking in visual surveillance. Due to the clutter of traffic scenes, the captured v...
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In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can pr...
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In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.
Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high...
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Implicit camera transfer (ICT), which models the multi-valued mappings between two specific and stationary cameras, is a descent solution for the person re-identification problem of the surveillance system. It has the...
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Neighborhood Preserving Embedding (NPE) is a subspace learning algorithm, which has the ability of preserving local neighborhood structure on the data manifold. Though NPE has been applied in many domains of pattern r...
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For the fast requirement of motion object detection under complex environment, a background subtraction motion object detection method based on real-time background update is presented in this paper.
ISBN:
(纸本)9781467389808
For the fast requirement of motion object detection under complex environment, a background subtraction motion object detection method based on real-time background update is presented in this paper.
A novel video scrambling algorithm based on generalized Fibonacci numbers is given in this paper. The experiment results show that the algorithm has better robust than the traditional video scrambling algorithm. Based...
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ISBN:
(纸本)9780889867024
A novel video scrambling algorithm based on generalized Fibonacci numbers is given in this paper. The experiment results show that the algorithm has better robust than the traditional video scrambling algorithm. Based on the property of uniformity of the corresponding Fibonacci transformation, the suggested method has the following advantages: (1) Encoding and decoding are very simple and they can be applied in real-time situations. (2) The algorithm can endure severe attacks such as extreme noise levels, very high loss in its data or data packets. (3) The algorithm is independent on any video format or encode mode.
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